Indicadores para monitoramento de pesquisa em saúde no Brasil
Bibliographic record
Abstract
No Brasil, o Ministério da Saúde é importante financiador e usuário de pesquisas e projetos de desenvolvimento tecnológico. No entanto, os mecanismos formais de acompanhamento do fomento a pesquisas são incipientes. O objetivo do trabalho foi propor indicadores para monitorar o fomento das pesquisas financiadas. As necessidades de informação foram identificadas, e os indicadores foram formulados baseados em visitas a órgãos de fomento à pesquisa no Brasil, revisão de literatura e reuniões técnicas. Os indicadores informam quais pesquisas são financiadas e qual a correlação com as prioridades da política de saúde; como ocorre a distribuição do financiamento; quais externalidades foram produzidas pela pesquisa. Discute-se a importância do monitoramento para designar recursos em pesquisas prioritárias. O uso dos indicadores pode guiar a construção de base de dados no Ministério da Saúde.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".